rename things

src/gpt_sovits/infer/__init__.py

@@ -1,3 +1,3 @@
-from gpt_sovits.infer.inference import GPTSoVITSInference
+from gpt_sovits.infer.worker import GPTSoVITSInference
 
 __all__ = ["GPTSoVITSInference"]

src/gpt_sovits/infer/api.py → src/gpt_sovits/infer/interface.py

@@ -1,4 +1,4 @@
-from gpt_sovits.infer.inference import GPTSoVITSInference
+from gpt_sovits.infer.worker import GPTSoVITSInference
 from pydantic import BaseModel
 from typing import List, Tuple, Optional
 from pathlib import Path
@@ -11,7 +11,7 @@ class ConfigData(BaseModel):
     prompts: List[str]
 
 
-class GPTSoVITSAPI:
+class GPTSoVITSInferenceSimple:
     config_data_base: Path
     config_data: ConfigData
     working_config: Tuple[str, str]
@@ -141,7 +141,7 @@ def generate_stream(
         Lock(),
     )
 
-    api = GPTSoVITSAPI(
+    api = GPTSoVITSInferenceSimple(
         config_data_base="config_data",
         inference_worker_and_lock=inference_worker_and_lock,
     )

src/gpt_sovits/infer/text_utils.py

@@ -0,0 +1,65 @@
+import re
+from typing import List
+
+
+splits = {
+    "，",
+    "。",
+    "？",
+    "！",
+    ",",
+    ".",
+    "?",
+    "!",
+    "~",
+    ":",
+    "：",
+    "—",
+    "…",
+}
+
+
+def cut5(inp: str):
+    """Cut one line of text into pieces."""
+    items = re.split(f"([{''.join(splits)}])", inp)
+    if items[-1] == "":
+        items = items[:-1]
+    if len(items) % 2 == 1:
+        items.append(".")
+
+    mergeitems: List[str] = [items[0]]
+    for item in items[1:]:
+        if item == "":
+            continue
+        if item not in splits:
+            mergeitems.append(item)
+        else:
+            mergeitems[-1] += item
+
+    return mergeitems
+
+
+def merge_short_texts(texts: List[str], threshold: int = 6):
+    """Merge short texts to longer ones. Texts are generated by cut5."""
+    result: List[str] = []
+    text = ""
+    for ele in texts:
+        text += ele
+        if len(text) >= threshold:
+            result.append(text)
+            text = ""
+    if text:
+        result.append(text)
+    return result
+
+
+def clean_and_cut_text(text: str) -> List[str]:
+    lines = [line.strip() for line in text.split("\n") if line.strip()]
+    texts = [
+        merged.strip()
+        for line in lines
+        for merged in merge_short_texts(cut5(line))
+        if not all(char in splits for char in merged.strip())
+    ]
+    texts = ["." + text if len(text) < 5 else text for text in texts]
+    return texts

src/gpt_sovits/infer/inference.py → src/gpt_sovits/infer/worker.py

@@ -1,4 +1,3 @@
-import re
 import sys
 import LangSegment
 import torch
@@ -8,7 +7,7 @@
 import sys
 import importlib.util
 from contextlib import contextmanager
-from typing import Optional, Any, TypeVar, cast, List
+from typing import Optional, Any, TypeVar, cast, List, Tuple
 from queue import Queue
 from transformers import AutoModelForMaskedLM, AutoTokenizer
 
@@ -19,6 +18,8 @@
 from gpt_sovits.text.cleaner import clean_text
 from gpt_sovits.module.mel_processing import spectrogram_torch
 
+from gpt_sovits.infer.text_utils import splits, clean_and_cut_text
+
 
 class DictToAttrRecursive(dict):
     def __init__(self, input_dict):
@@ -72,57 +73,6 @@ def clean_text_inf(text, language):
     return phones, word2ph, norm_text
 
 
-splits = {
-    "，",
-    "。",
-    "？",
-    "！",
-    ",",
-    ".",
-    "?",
-    "!",
-    "~",
-    ":",
-    "：",
-    "—",
-    "…",
-}
-
-
-def cut5(inp: str):
-    """Cut one line of text into pieces."""
-    items = re.split(f"([{''.join(splits)}])", inp)
-    if items[-1] == "":
-        items = items[:-1]
-    if len(items) % 2 == 1:
-        items.append(".")
-
-    mergeitems: List[str] = [items[0]]
-    for item in items[1:]:
-        if item == "":
-            continue
-        if item not in splits:
-            mergeitems.append(item)
-        else:
-            mergeitems[-1] += item
-
-    return mergeitems
-
-
-def merge_short_texts(texts: List[str], threshold: int = 6):
-    """Merge short texts to longer ones. Texts are generated by cut5."""
-    result: List[str] = []
-    text = ""
-    for ele in texts:
-        text += ele
-        if len(text) >= threshold:
-            result.append(text)
-            text = ""
-    if text:
-        result.append(text)
-    return result
-
-
 class GPTSoVITSInference:
     device: str
     is_half: bool
@@ -321,7 +271,7 @@ def _get_spepc(self):
     @property
     def zero_wav(self):
         return np.zeros(
-            int(self.sample_rate * 0.25),
+            int(self.sample_rate * 0.3),
             dtype=self.np_dtype,
         )
 
@@ -385,14 +335,14 @@ def set_prompt_audio(
             self.phones1 = None
             self.bert1 = None
 
-    def _get_tts_wav(
+    def get_tts_wav_piece(
         self,
         text: str,
         text_language: str = "auto",
         top_k=5,
         top_p=1,
         temperature=1,
-    ):
+    ) -> Tuple[int, np.ndarray]:
         phones2, bert2, norm_text2 = self._get_phones_and_bert(text, text_language)
         if self.prompt_text:
             bert = torch.cat([self.bert1, bert2], 1)
@@ -434,7 +384,9 @@ def _get_tts_wav(
         max_audio = np.abs(audio).max()  # 简单防止16bit爆音
         if max_audio > 1:
             audio /= max_audio
-        return audio
+        return self.sample_rate, (
+            np.concatenate((audio, self.zero_wav)) * 32768
+        ).astype(np.int16)
 
     def produce_tts_wav(
         self,
@@ -445,25 +397,16 @@ def produce_tts_wav(
         top_p=1,
         temperature=1,
     ):
-        lines = [line.strip() for line in text.split("\n") if line.strip()]
-        texts = [
-            merged.strip()
-            for line in lines
-            for merged in merge_short_texts(cut5(line))
-            if not all(char in splits for char in merged.strip())
-        ]
-        texts = ["." + text if len(text) < 5 else text for text in texts]
+        texts = clean_and_cut_text(text)
         for text in texts:
-            audio = self._get_tts_wav(
+            _, audio = self.get_tts_wav_piece(
                 text,
                 text_language,
                 top_k,
                 top_p,
                 temperature,
             )
-            queue.put(
-                (np.concatenate((audio, self.zero_wav), 0) * 32768).astype(np.int16)
-            )
+            queue.put(audio)
         queue.put(None)
 
     def get_tts_wav_stream(
@@ -494,18 +437,24 @@ def get_tts_wav(
         text_language="auto",
         top_k=5,
         top_p=1,
-        temperature=0.9,
+        temperature=1,
     ):
         audio_opt = []
-        for _, audio in self.get_tts_wav_stream(
-            text,
-            text_language,
-            top_k,
-            top_p,
-            temperature,
-        ):
-            audio_opt.append(audio)
-        return self.sample_rate, np.concatenate(audio_opt, 0)
+        texts = clean_and_cut_text(text)
+        audio_opt = [
+            self.get_tts_wav_piece(
+                text,
+                text_language,
+                top_k,
+                top_p,
+                temperature,
+            )[1]
+            for text in texts
+        ]
+        return self.sample_rate, np.concatenate(audio_opt)
+
+
+Worker = GPTSoVITSInference
 
 
 if __name__ == "__main__":